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np1d.py
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np1d.py
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import numpy as np, Image
from matplotlib.colors import hsv_to_rgb, rgb_to_hsv
import matplotlib.pyplot as plt
# params
nbins = 32
size = (512,512)
just_rgb = False
hcut = 0.0 # histogram cutoff min
def open_hsv(fname, size=(256,256)):
img = Image.open(fname)
img.thumbnail(size)
ary = np.asarray(img)/255.0
# xform to hsv
if just_rgb:
hsv = ary
else:
hsv = rgb_to_hsv(ary)
return hsv
def rolling_window ( a , window ):
shape = a . shape [: - 1 ] + ( a . shape [ - 1 ] - window + 1 , window )
strides = a . strides + ( a . strides [ - 1 ],)
return np . lib . stride_tricks . as_strided ( a , shape = shape , strides = strides )
def gen_map(vals, n=nbins, hue=False):
# saturation
fvals = vals.flatten()
yh, xh, patches = plt.hist(fvals, bins=n, range=(0,1), normed=False , cumulative=False , histtype='step')
if hue:
# apply window
M = 9
win = np.kaiser(M, 3.0)
yh = np.insert(yh, 0, np.zeros(M/2))
yh = np.append(yh, np.zeros(M/2))
yh = rolling_window(yh.T, M)
yh = np.dot(yh, win)
yh /= sum(yh)
if hue:
# adapted norm
#yh = np.minimum(yh, hcut)
yh[yh<=hcut] = 0
yh /= sum(yh)
yh = np.cumsum(yh)
xhi = np.linspace(0,1,256)
yhi = np.interp(xhi, yh, xh[1:])
yhinv = np.interp(xhi, xh[1:], yh)
#plt.plot(xhi, yhi)
return (yhi, yhinv)
def apply_map(vals, map, bal=1.0, hue=False):
hi = vals*255.0
hi = hi.astype(np.uint8, copy=False)
if not hue:
vals_t = map[hi]*bal + vals*(1-bal)
else:
mhi = map[hi] - 0.5
#mhi[mhi>0.5] -= 1.0
vhi = vals - 0.5
#vhi[vhi>0.5] -= 1.0
vals_t = mhi*bal + vhi*(1-bal) + 0.5
#vals_t[vals_t<0] += 1.0
return vals_t
def chain_maps(a, b):
return (a + b) / 2.0
# old:
c = np.empty((3,256))
for r in [0,1,2]:
i = a[r] * 255.0
i = i.astype(np.uint8, copy=False)
c[r,:] = b[r,i]
#c[1,0:50] = np.linspace(0,0.1,50)
#c[1,:] = c[1,:] * np.power(np.linspace(0,1.0,256), 0.3)
return c
def gen_linmaps():
out = np.empty((3,256))
out[0,:] = np.linspace(0,1,256)
out[1,:] = np.linspace(0,1,256)
out[2,:] = np.linspace(0,1,256)
return out
def gen_maps(hsv):
out = np.empty((3,256))
inv = np.empty((3,256))
# gen output arrays
(out[0,:], inv[0,:]) = gen_map(hsv[...,0].flatten(), hue=True)
(out[1,:], inv[1,:]) = gen_map(hsv[...,1].flatten())
(out[2,:], inv[2,:]) = gen_map(hsv[...,2].flatten())
return (out, inv)
def apply_maps(out, maps, bal=(1.0,1.0,1.0)):
res = np.empty_like(out)
res[...,0] = apply_map(out[...,0], maps[0], bal[0], hue=not just_rgb)
res[...,1] = apply_map(out[...,1], maps[1], bal[1])
res[...,2] = apply_map(out[...,2], maps[2], bal[2])
return res
if __name__ == '__main__':
# input image
hsv = open_hsv('orig/dunes.jpg')
# output image
out_hsv = open_hsv('orig/karussel.jpg', size=size)
(maps, imaps) = gen_maps(hsv)
#plt.show()
plt.clf()
(out_maps, iout_maps) = gen_maps(out_hsv)
#plt.show()
plt.clf()
xm = gen_linmaps()
x = xm[0]
#bal=(0.6,0.8,1.0)
bal=(1.0,1.0,1.0)
#maps2 = chain_maps(out_maps, maps)
maps2 = maps
# color cycle: bgr = hsv
plt.plot(x, maps2.T)
plt.show()
hsv_out = apply_maps(out_hsv, maps2, bal=bal)
if just_rgb:
rgb = hsv_out * 255
else:
rgb = hsv_to_rgb(hsv_out)*255
hsv = hsv_out * 255
nimg = Image.fromarray(rgb.astype(np.uint8))
hsvimg = Image.fromarray(hsv.astype(np.uint8))
nimg.show()
'''
# composite
import ui
isize = nimg.size
nimg.save('tmp.jpg')
uiimg = ui.Image.named('tmp.jpg')
orig = ui.Image.named('orig/dunes.jpg')
with ui.ImageContext(*isize) as ctx:
orig.draw(0,0,*isize)
ui.set_blend_mode(ui.BLEND_MULTIPLY)
uiimg.draw(0,0,200,200)
ui.set_blend_mode(ui.BLEND_COLOR)
ui.fill_rect(0, 0, 100, 100)
cimg = ctx.get_image()
cimg.show()
'''